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Impact of meteorological parameters on aerosol optical depth and particulate matter in Lahore

100%

Ur Rehman Z. , Tariq S. , ul Haq Z. , Khan M.

tom Vol. 72, no. 2

1377--1395

Particulate pollution has become one of the major issues in the mega-cities of Pakistan. As with the increase in rapid urbanization, poor air quality, climate change, and health-related issues are increasing gradually in Lahore. Therefore, the implications for the variability of air pollution need to be better understood for the improvement of air quality. So, in this article, we used aerosol robotic network (AERONET) and moderate resolution imaging spectroradiometer (MODIS) datasets along with the variability of different meteorological parameters (temperature, wind speed, relative humidity, dew point, and sea level pressure) over Lahore during 2006 to 2022. Moreover, the multi-linear regression model is used to analyse the linear relation of AERONET-retrieved aerosol optical depth (AOD) and particulate matter (PM2.5) with MODIS-retrieved AOD during the time period. Both AOD and PM2.5 increase gradually throughout the time period. AERONET-retrieved AOD showed a significant variability during the time period where each meteorological parameter gives a significant value (p < 0.05) except pressure (p > 0.05). The AERONET-retrieved AOD and PM2.5 give a strong positive value (0.78 and 0.63) of the coefficient of correlation. Seasonally, the value of the coefficient of correlation is observed high during summer (0.92) followed by autumn, spring, and winter. Considering the outcomes of this study, different methods like using better quality of fuel, use of public transport, plantation of trees, etc., can be employed to reduce air pollution.

Streamflow prediction using data-driven models: Case study of Wadi Hounet, northwestern Algeria

85%

Beddal D. , Achite M. , Baahmed D.

tom no. 47

16--24

Streamflow modelling is a very important process in the management and planning of water resources. However, complex processes associated with the hydro-meteorological variables, such as non-stationarity, non-linearity, and randomness, make the streamflow prediction chaotic. The study developed multi linear regression (MLR) and back propagation neural network (BPNN) models to predict the streamflow of Wadi Hounet sub-basin in north-western Algeria using monthly hydrometric data recorded between July 1983 and May 2016. The climatological inputs data are rainfall (P) and reference evapotranspiration (ETo) on a monthly scale. The outcomes for both BPNN and MLR models were evaluated using three statistical measurements: Nash–Sutcliffe efficiency coefficient (NSE), the coefficient of correlation (R) and root mean square error (RMSE). Predictive results revealed that the BPNN model exhibited good performance and accuracy in the prediction of streamflow over the MLR model during both training and validation phases. The outcomes demonstrated that BPNN-4 is the best performing model with the values of 0.885, 0.941 and 0.05 for NSE, R and RMSE, respectively. The highest NSE and R values and the lowest RMSE for both training and validation are an indication of the best network. Therefore, the BPNN model provides better prediction of the Hounet streamflow due to its capability to deal with complex nonlinearity procedures.